sreeharivp23/fire-segmentation-yolo11n
Image Segmentation • Updated • 47
Datasets:
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The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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0 0.020000 0.494048 0.013333 0.547619 0.000000 0.553571 0.013333 0.559524 0.013333 0.690476 0.023333 0.726190 0.020000 0.928571 0.036667 0.892857 0.106667 0.916667 0.183333 0.910714 0.200000 0.845238 0.186667 0.767857 0.220000 0.636905 0.236667 0.636905 0.246667 0.678571 0.286667 0.726190 0.286667 0.750000 0.273333 0.7... |
0 0.623729 0.402857 0.610169 0.420000 0.593220 0.468571 0.593220 0.511429 0.588136 0.540000 0.588136 0.562857 0.593220 0.582857 0.605085 0.602857 0.628814 0.605714 0.644068 0.600000 0.650847 0.568571 0.661017 0.560000 0.666102 0.548571 0.674576 0.545714 0.681356 0.537143 0.686441 0.522857 0.688136 0.494286 0.696610 0.4... |
0 0.325424 0.297143 0.305085 0.322857 0.305085 0.340000 0.318644 0.348571 0.320339 0.371429 0.318644 0.382857 0.305085 0.411429 0.308475 0.451429 0.301695 0.477143 0.301695 0.497143 0.306780 0.511429 0.301695 0.562857 0.305085 0.571429 0.316949 0.577143 0.349153 0.577143 0.357627 0.594286 0.359322 0.628571 0.371186 0.6... |
0 0.336667 0.000000 0.363333 0.190476 0.363333 0.363095 0.380000 0.375000 0.396667 0.422619 0.430000 0.416667 0.443333 0.375000 0.476667 0.380952 0.496667 0.357143 0.513333 0.357143 0.550000 0.488095 0.663333 0.500000 0.653333 0.357143 0.613333 0.345238 0.563333 0.279762 0.536667 0.267857 0.513333 0.291667 0.496667 0.2... |
0 0.666667 0.565476 0.663333 0.571429 0.663333 0.595238 0.666667 0.601190 0.683333 0.601190 0.683333 0.565476 |
End of preview.
Fire Segmentation Dataset (YOLO-seg format)
Instance-segmentation dataset for fire detection: 1,348 images with polygon mask labels in Ultralytics YOLO segmentation format. Built to train sreeharivp23/fire-segmentation-yolo11n.
Contents
| Split | Images |
|---|---|
| train | 1,146 |
| val | 202 |
- 1,098 fire images with one or more
firepolygon instances - 250 negatives (no fire) with empty label files
fire_seg/
├── data.yaml # Ultralytics dataset config (1 class: fire)
├── images/{train,val}/ # .jpg / .png images
└── labels/{train,val}/ # YOLO-seg .txt labels: "0 x1 y1 x2 y2 ..." (normalised)
Usage
from huggingface_hub import snapshot_download
from ultralytics import YOLO
path = snapshot_download("sreeharivp23/fire-segmentation-dataset", repo_type="dataset")
model = YOLO("yolo11n-seg.pt")
model.train(data=f"{path}/data.yaml", epochs=60, imgsz=640)
Note:
data.yamlcontains apath:entry pointing at the original build location — update it to the downloaded folder (or delete the line and pass absolute paths) before training.
How the labels were made
Labels are machine-generated, not human-annotated:
- A pretrained YOLOv8 fire/smoke bounding-box detector proposed fire regions.
- SAM 2.1 (base) converted each box into a pixel mask.
- Masks were polygonised (OpenCV contours + Douglas-Peucker) into YOLO-seg polygons.
- Images the detector missed fell back to an HSV flame-colour heuristic (219 images); 12 images yielded nothing and were dropped.
Expect some label noise around smoke/glow boundaries and small flames. A YOLO11n-seg fine-tuned on this data reaches mask mAP50 ≈ 0.54 on the val split.
Image sources & licensing
Images originate from two public research datasets; this repackaging adds machine-generated segmentation labels:
Check the upstream repositories for their exact terms before commercial use.
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Models trained or fine-tuned on sreeharivp23/fire-segmentation-dataset